Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes

P Hurskainen,H Adhikari,M Siljander,P.K.E Pellikka,A Hemp

doi:10.1016/j.rse.2019.111354

Abstract

Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years.We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements.The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8 m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features.Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree.Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.

Highlights

Explicit land use and land cover (LULC) information at local, regional and global scales is one of the key variables needed for numerous environmental applications, such as assessing patterns of biodiversity, management of natural resources, prioritizing conservation activities and mapping environmental degradation (Wulder et al, 2018; Yu et al, 2014)
We found satisfactory segmentation result across different LULC types with scale parameter 9, shape 0.1 and compactness 0.25
Since we aimed for more parsimonious model, we chose to accept a minor loss in accuracy for less features by choosing the smallest subset size with > 1.0% allowed loss in overall accuracy (OA) as compared to the numerically best subset size

Summary

Introduction

Explicit land use and land cover (LULC) information at local, regional and global scales is one of the key variables needed for numerous environmental applications, such as assessing patterns of biodiversity, management of natural resources, prioritizing conservation activities and mapping environmental degradation (Wulder et al, 2018; Yu et al, 2014). Integration of Earth observation and other spatial data from multiple sources is required to derive this information with sufficient accuracy and level of detail over large areas (Herold et al, 2016). Use of multispectral single-date satellite data alone is not sufficient, especially in landscapes characterized with heterogeneous, discontinuous vegetation structure and extensive human-induced LULC disturbance (Strahler, 1981; Bruzzone et al, 1997; Rodriguez-Galiano and Chica-Olmo, 2012). In such landscapes, LULC types are often difficult to separate spectrally due to low inter-class separability and high. In African savanna vegetation structure and pattern is heterogeneous (Mishra and Crews, 2014) and dynamic driven by high spatio-temporal variability in rainfall, soils, geomorphology, herbivory, fire history and anthropogenic disturbance (Coughenour and Ellis, 1993)

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